by Michael Grogan | Released January 2018 | ISBN: 9781492028543

Learn from data science expert Michael Grogan in this tutorial that teaches you how to use regression analysis and R to uncover high-value business insights hidden inside large datasets. The course reviews the meaning of regression analysis; shows you how to use R to conduct regression analysis techniques on cross-sectional and time series datasets; discusses standard regression techniques such as Ordinary Least Squares (OLS) and Logistic Regressions; and surveys the various violations of OLS and how these can be corrected. By the end of the course, you'll understand the theory behind regression analysis and how to put this theory into practice. Learners should have a basic understanding of statistics, familiarity with data types (i.e., nominal, ordinal, interval, and scale), and preferably some prior experience with R.Learn to uncover key business insights hidden inside data using regression analysis and RGain hands-on experience running linear and logistic regressions using RUnderstand how to interpret statistical output and derive meaning from resultsExplore methods that screen and correct for violations of Ordinary Least Squares assumptionsMichael Grogan is a data scientist who specializes in R, Python, and Shiny. As a consultant, Michael provides data science solutions to clients in healthcare, finance, and government. As an educator, Michael creates data science tutorials for organizations such as Data Science Central, Sitepoint, and O'Reilly Media. He holds a Master's degree in business economics from University College Cork. Show and hide more Publisher resources Download Example Code

1. Introduction
   • Welcome to the Course 00:01:25
   • About the Author 00:01:31
2. Introduction to Regression Analysis
   • Introduction to Regression Analysis 00:01:59
   • Interpretation of Regression Output 00:08:31
3. OLS Violations and Logistic Regressions
   • Multicollinearity 00:03:33
   • Normality 00:05:25
   • Heteroscedasticity 00:05:16
   • Logistic Regressions 00:03:53
4. Working With Time Series
   • Stationarity and Serial Correlation 00:10:23
   • ARIMA Modelling and Forecasting 00:02:48
   • Forecast Validation and the Ljung-Box test 00:02:34
5. Conclusion
   • Wrap Up and Thank You 00:01:24
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